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Cosine alignment in vision-language models may mislead researchers, as it correlates negatively with accuracy, revealing that latents are often bypassed in reasoning.
LLMs and LVLMs share more than half their top-activated neurons during multi-step inference, opening a surprisingly cheap path to boost vision-language reasoning by transplanting skills from text-only models.